Side-by-side comparison of AI visibility scores, market position, and capabilities
Kibo Commerce is a composable unified commerce platform for order management and personalization, raised $100M+ and based in Dallas, TX.
Kibo Commerce was formed through the combination of Certona, Monetate, and Kibo, three established retail technology companies that merged to create a unified commerce platform covering personalization, order management, and e-commerce capabilities. The company, headquartered in Dallas, Texas and backed by over $100M in funding, targets mid-market and enterprise retailers looking for a composable alternative to monolithic commerce suites from vendors like Salesforce Commerce Cloud or SAP Commerce.\n\nKibo's platform provides a distributed order management system, a headless e-commerce engine, and an AI-driven personalization engine in a modular suite that retailers can deploy together or independently. The order management component handles real-time inventory visibility, order routing, fulfillment orchestration, and returns management across complex retail networks. The personalization engine, drawing on the Certona and Monetate heritage, powers product recommendations, content personalization, and A/B testing across digital touchpoints.\n\nKibo competes in the challenger tier of the order management and composable commerce markets, targeting retailers that want enterprise-grade capabilities without the implementation complexity and cost of legacy platforms. The company's composable architecture aligns with the growing MACH (Microservices, API-first, Cloud-native, Headless) movement in retail technology, positioning it as a flexible foundation for retailers modernizing their commerce infrastructure.
a2z Radiology AI raised $20M in 2025 for its whole-body AI that simultaneously screens for 24+ conditions across CT scans — from incidental cancers to cardiovascular risk — in a single automated read.
a2z Radiology AI has developed a whole-body CT analysis platform that simultaneously screens for over 24 medical conditions across a single CT scan, including incidental cancers, coronary artery disease, aortic aneurysm, bone density loss, and organ abnormalities. The AI acts as a second reader that radiologists can use to catch incidental findings that fall outside the primary reason for a scan — a major source of missed diagnoses.
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